from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-28 14:08:35.124302
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Mon, 28, Dec, 2020
Time: 14:08:38
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.2132
Nobs: 154.000 HQIC: -45.2672
Log likelihood: 1664.43 FPE: 1.06743e-20
AIC: -45.9881 Det(Omega_mle): 6.05944e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.460396 0.160076 2.876 0.004
L1.Burgenland 0.140247 0.081506 1.721 0.085
L1.Kärnten -0.239948 0.065707 -3.652 0.000
L1.Niederösterreich 0.109379 0.189679 0.577 0.564
L1.Oberösterreich 0.260815 0.162137 1.609 0.108
L1.Salzburg 0.175832 0.084273 2.086 0.037
L1.Steiermark 0.076020 0.116659 0.652 0.515
L1.Tirol 0.153864 0.077825 1.977 0.048
L1.Vorarlberg -0.000428 0.074927 -0.006 0.995
L1.Wien -0.119100 0.156865 -0.759 0.448
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.517274 0.208064 2.486 0.013
L1.Burgenland 0.012832 0.105940 0.121 0.904
L1.Kärnten 0.361359 0.085405 4.231 0.000
L1.Niederösterreich 0.127612 0.246541 0.518 0.605
L1.Oberösterreich -0.189738 0.210743 -0.900 0.368
L1.Salzburg 0.192477 0.109536 1.757 0.079
L1.Steiermark 0.246754 0.151631 1.627 0.104
L1.Tirol 0.144578 0.101156 1.429 0.153
L1.Vorarlberg 0.182837 0.097388 1.877 0.060
L1.Wien -0.579106 0.203890 -2.840 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.295529 0.069785 4.235 0.000
L1.Burgenland 0.107701 0.035533 3.031 0.002
L1.Kärnten -0.028093 0.028645 -0.981 0.327
L1.Niederösterreich 0.066016 0.082690 0.798 0.425
L1.Oberösterreich 0.292547 0.070684 4.139 0.000
L1.Salzburg -0.003382 0.036739 -0.092 0.927
L1.Steiermark -0.023061 0.050858 -0.453 0.650
L1.Tirol 0.089487 0.033928 2.638 0.008
L1.Vorarlberg 0.128663 0.032664 3.939 0.000
L1.Wien 0.081460 0.068385 1.191 0.234
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.200485 0.080958 2.476 0.013
L1.Burgenland -0.010050 0.041221 -0.244 0.807
L1.Kärnten 0.019325 0.033231 0.582 0.561
L1.Niederösterreich 0.024168 0.095929 0.252 0.801
L1.Oberösterreich 0.410588 0.082001 5.007 0.000
L1.Salzburg 0.098665 0.042621 2.315 0.021
L1.Steiermark 0.180796 0.059000 3.064 0.002
L1.Tirol 0.034617 0.039360 0.880 0.379
L1.Vorarlberg 0.099084 0.037894 2.615 0.009
L1.Wien -0.058717 0.079334 -0.740 0.459
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.587951 0.168569 3.488 0.000
L1.Burgenland 0.068451 0.085830 0.798 0.425
L1.Kärnten 0.003648 0.069193 0.053 0.958
L1.Niederösterreich -0.045327 0.199742 -0.227 0.820
L1.Oberösterreich 0.158182 0.170739 0.926 0.354
L1.Salzburg 0.052701 0.088744 0.594 0.553
L1.Steiermark 0.115075 0.122848 0.937 0.349
L1.Tirol 0.216726 0.081954 2.644 0.008
L1.Vorarlberg 0.006467 0.078902 0.082 0.935
L1.Wien -0.146895 0.165187 -0.889 0.374
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.164365 0.117637 1.397 0.162
L1.Burgenland -0.024864 0.059897 -0.415 0.678
L1.Kärnten -0.015948 0.048287 -0.330 0.741
L1.Niederösterreich 0.171564 0.139391 1.231 0.218
L1.Oberösterreich 0.394684 0.119152 3.312 0.001
L1.Salzburg -0.027955 0.061931 -0.451 0.652
L1.Steiermark -0.050046 0.085731 -0.584 0.559
L1.Tirol 0.191991 0.057192 3.357 0.001
L1.Vorarlberg 0.041368 0.055062 0.751 0.452
L1.Wien 0.163768 0.115277 1.421 0.155
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.219694 0.146248 1.502 0.133
L1.Burgenland 0.075073 0.074465 1.008 0.313
L1.Kärnten -0.048556 0.060031 -0.809 0.419
L1.Niederösterreich -0.037826 0.173293 -0.218 0.827
L1.Oberösterreich -0.113709 0.148131 -0.768 0.443
L1.Salzburg 0.007551 0.076993 0.098 0.922
L1.Steiermark 0.384205 0.106581 3.605 0.000
L1.Tirol 0.521612 0.071102 7.336 0.000
L1.Vorarlberg 0.214066 0.068454 3.127 0.002
L1.Wien -0.219629 0.143314 -1.532 0.125
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.108293 0.171246 0.632 0.527
L1.Burgenland 0.024186 0.087193 0.277 0.781
L1.Kärnten -0.116501 0.070292 -1.657 0.097
L1.Niederösterreich 0.221414 0.202914 1.091 0.275
L1.Oberösterreich 0.005088 0.173451 0.029 0.977
L1.Salzburg 0.221977 0.090153 2.462 0.014
L1.Steiermark 0.141639 0.124799 1.135 0.256
L1.Tirol 0.097116 0.083256 1.166 0.243
L1.Vorarlberg 0.023290 0.080155 0.291 0.771
L1.Wien 0.284139 0.167811 1.693 0.090
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.584268 0.094890 6.157 0.000
L1.Burgenland -0.019733 0.048315 -0.408 0.683
L1.Kärnten -0.001182 0.038950 -0.030 0.976
L1.Niederösterreich -0.010486 0.112438 -0.093 0.926
L1.Oberösterreich 0.280913 0.096112 2.923 0.003
L1.Salzburg 0.010969 0.049955 0.220 0.826
L1.Steiermark -0.002735 0.069153 -0.040 0.968
L1.Tirol 0.080257 0.046134 1.740 0.082
L1.Vorarlberg 0.174599 0.044415 3.931 0.000
L1.Wien -0.091923 0.092987 -0.989 0.323
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.142233 -0.010733 0.203434 0.243817 0.052353 0.097105 -0.089573 0.161718
Kärnten 0.142233 1.000000 -0.009033 0.183526 0.133116 -0.148036 0.171477 0.029264 0.297098
Niederösterreich -0.010733 -0.009033 1.000000 0.257087 0.079967 0.195463 0.092848 0.030494 0.347499
Oberösterreich 0.203434 0.183526 0.257087 1.000000 0.278092 0.290299 0.094263 0.064496 0.100374
Salzburg 0.243817 0.133116 0.079967 0.278092 1.000000 0.144563 0.061742 0.073946 -0.029202
Steiermark 0.052353 -0.148036 0.195463 0.290299 0.144563 1.000000 0.096371 0.080628 -0.138521
Tirol 0.097105 0.171477 0.092848 0.094263 0.061742 0.096371 1.000000 0.136850 0.126670
Vorarlberg -0.089573 0.029264 0.030494 0.064496 0.073946 0.080628 0.136850 1.000000 0.093305
Wien 0.161718 0.297098 0.347499 0.100374 -0.029202 -0.138521 0.126670 0.093305 1.000000